Jean Kossaifi ![](/sites/default/files/person/Jean.jpg) Jean Kossaifi leads research at NVIDIA in the field of AI for Scientific Simulation, where he advances new algorithmic paradigms to solve complex physics-based problems. His core research focuses on fundamental algorithms, including combining tensor methods with deep learning, to develop efficient and powerful neural architectures. To help democratize advanced computational techniques and accelerate scientific discovery, he created two widely used open-source libraries: [*TensorLy*](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Ftensorly%2Ftensorly&data=05%7C02%7Cjkossaifi%40nvidia.com%7C6f7dbbb2e4d144ef16dc08ddcec0e168%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638894050010904262%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=ccxvS9vmVscHEhtG0sIBHYEfurqgMq1YgonNu8PWHEw%3D&reserved=0), for tensor methods, and [*NeuralOperator*](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fneuraloperator%2Fneuraloperator&data=05%7C02%7Cjkossaifi%40nvidia.com%7C6f7dbbb2e4d144ef16dc08ddcec0e168%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638894050010919548%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=KABrEMc9X1plv6Vehv%2Fizu766l7ZNdnYBFhep6bwp0I%3D&reserved=0), for scientific machine learning. Prior to NVIDIA, Jean was a founding member of the Samsung AI Center in Cambridge. His academic foundation includes a French Engineering Diploma in Mathematics, Computer Science, and Finance and a BSc in advanced mathematics. Jean then completed my PhD in Artificial Intelligence at Imperial College London. For more on his work, including his publications and open-source projects, please visit his [personal website](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fjeankossaifi.com%2F&data=05%7C02%7Cjkossaifi%40nvidia.com%7C6f7dbbb2e4d144ef16dc08ddcec0e168%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638894050010934019%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=%2BktFD6Rk5iLpqETl5QrEEYxluCuJQgMNExjjlPubKjc%3D&reserved=0) and [Google Scholar](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.fr%2Fcitations%3Fhl%3Den%26user%3DhJS2TXwAAAAJ&data=05%7C02%7Cjkossaifi%40nvidia.com%7C6f7dbbb2e4d144ef16dc08ddcec0e168%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638894050010948537%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=WAqcXsbw4RYJ1n9H8EhVZLrgEQfHNnhhYI%2Fw2%2BEp7mI%3D&reserved=0) profile. Research Area(s) [Computer Vision](/index.php/research-area/computer-vision) [Human Computer Interaction](/index.php/research-area/human-computer-interaction) [Physical AI](/index.php/research-area/physical-ai) Main Field of Interest [Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence) Google Scholar ### Publications ### 2026 [Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting](/publication/2026-01_demystifying-data-driven-probabilistic-medium-range-weather-forecasting) [Jean Kossaifi](/person/jean-kossaifi), [Nikola Kovachki](/person/nikola-kovachki), [Morteza Mardani](/person/morteza-mardani), [Daniel Leibovici](/person/daniel-leibovici), Suman Ravuri, Ira Shokar, Edoardo Calvello, Mohammad Shoaib Abbas, Peter Harrington, Ashay Subramaniam, [Noah Brenowitz](/person/noah-brenowitz), [Boris Bonev](/person/boris-bonev), [Wonmin Byeon](/person/wonmin-byeon), [Karsten Kreis](/person/karsten-kreis), [Dale Durran](/person/dale-durran), [Arash Vahdat](/person/arash-vahdat), [Mike Pritchard](/person/mike-pritchard), [Jan Kautz](/person/jan-kautz) ### 2025 [FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale](/publication/2025-07_fourcastnet-3-geometric-approach-probabilistic-machine-learning-weather) [Boris Bonev](/person/boris-bonev), Thorsten Kurth, Ankur Mahesh, Mauro Bisson, [Jean Kossaifi](/person/jean-kossaifi), Karthik Kashinath, Anima Anandkumar, William D. Collins, [Mike Pritchard](/person/mike-pritchard), [Alex Keller](/person/alex-keller) [Score-based Diffusion Models in Function Space](/index.php/publication/2025-07_score-based-diffusion-models-function-space) Jae Hyun Lim, [Nikola Kovachki](/index.php/person/nikola-kovachki), Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, [Jean Kossaifi](/index.php/person/jean-kossaifi), Vikram Voleti, Jiaming Song, [Karsten Kreis](/index.php/person/karsten-kreis), [Jan Kautz](/index.php/person/jan-kautz), Christopher Pal, [Arash Vahdat](/index.php/person/arash-vahdat), Anima Anandkumar [Journal of Machine Learning Research (JMLR) 2025](https://arxiv.org/abs/2302.07400) ### 2024 [Pretraining codomain attention neural operators for solving multiphysics pdes](/publication/2024-12_pretraining-codomain-attention-neural-operators-solving-multiphysics-pdes) Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy, Daniel Leibovici, Zongyi Li, [Boris Bonev](/person/boris-bonev), Colin White, Julius Berner, Raymond A. Yeh, [Jean Kossaifi](/person/jean-kossaifi), Kamyar Azizzadenesheli, Anima Anandkumar [NeurIPS Proceedings](https://proceedings.neurips.cc/paper_files/paper/2024/hash/bc75fa9843a7905bbed9d83895a88f7f-Abstract-Conference.html) ### 2023 [Quantum Goemans-Williamson Algorithm with the Hadamard Test and Approximate Amplitude Constraints](/index.php/publication/2023-07_quantum-goemans-williamson-algorithm-hadamard-test-and-approximate-amplitude) [Taylor Patti](/index.php/person/taylor-patti), [Jean Kossaifi](/index.php/person/jean-kossaifi), Anima Anandkumar, Susanne F. Yelin [Towards a scalable discrete quantum generative adversarial neural network](/publication/2023-04_towards-scalable-discrete-quantum-generative-adversarial-neural-network) Smit Chaudhary, Patrick Huembeli, Ian MacCormack, [Taylor Patti](/person/taylor-patti), [Jean Kossaifi](/person/jean-kossaifi), Alexey Galda ### 2022 [HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression](/index.php/publication/2022-12_heat-hardware-efficient-automatic-tensor-decomposition-transformer-compression) Jiaqi Gu, [Ben Keller](/index.php/person/ben-keller), [Jean Kossaifi](/index.php/person/jean-kossaifi), Anima Anandkumar, [Brucek Khailany](/index.php/person/brucek-khailany), David Z. Pan [Workshop on ML for Systems at NeurIPS](http://mlforsystems.org) Spotlight Paper [Variational quantum optimization with multibasis encodings](/index.php/publication/2022-08_variational-quantum-optimization-multibasis-encodings) [Taylor Patti](/index.php/person/taylor-patti), [Jean Kossaifi](/index.php/person/jean-kossaifi), Anima Anandkumar, Susanne F. Yelin ### 2020 [Convolutional Tensor-Train LSTM for Spatio-Temporal Learning](/publication/2020-12_convolutional-tensor-train-lstm-spatio-temporal-learning) Jiahao Su, [Wonmin Byeon](/person/wonmin-byeon), [Jean Kossaifi](/person/jean-kossaifi), Furong Huang, [Jan Kautz](/person/jan-kautz), Anima Anandkumar [Advances in Neural Information Processing Systems (NeurIPS)](https://papers.nips.cc/paper/2020/hash/9e1a36515d6704d7eb7a30d783400e5d-Abstract.html)